The detection of lesions within a patient's body is an important task during medical examinations, in particular during medical examinations using imaging methods, like computer tomography (CT) or magnetic resonance imaging (MRI). An efficient and accurate detection of lesions supports the success rate of medical screenings and the quality of medical treatments. Especially in the case of cancer screenings and cancer treatments, the detection of lesions inside medical images and their classification as either benign or malign is a obvious and regular but crucial medical task in the daily clinical routine. For example, in the case of bone lesions, a regular analysis of the location, the number and the geometrical dimensions of malign lesions is required to evaluate the progression of metastases and the response of a patient to a certain medical treatment. An accurate analysis enables a successful pain management and can improve the probability of survival for a patient significantly.
Normally, a medical expert who has gained experience in his profession over many years, is performing such a detection by reviewing the medical images of the patient. Hereby, regions of interest are identified by the medical expert and analyzed in detail for any abnormalities which are an indication of benign or malign lesions. Specifically, the medical expert identifies the size, the shape and margin definition of a suspicious region inside a medical image as a basis for his evaluations. However, during a typical medical imaging sequence, a larger number of medical image is taken in many cases. Therefore, the manual analysis by a medical experts becomes a time-consuming activity, also leading to higher medical expenses for those tasks. Furthermore, it is evident that the quality of an analysis performed by experts largely depends on their respective expertise and experience, on the time spent for each analysis and on the scrutiny applied during an analysis. Accordingly, the quality of lesion detection and lesion analysis is characterized by intra- and inter-expert variations.
To address those variations, some proposals to replace or support medical experts by computer-aided detection and analysis techniques have been published already. For example, Wels et al. in “Multi-Stage Osteolytic Spinal Bone Lesion Detection from CT Data with Internal Sensitivity Control”, SPIE Medical Imaging, Vol. 8315, 2012 are suggesting a computer-aided detection of lesions located in the spine of a patient. Nevertheless, it is expected that such detection methods are limited and cannot achieve the quality of a detection and analysis of an experienced medical expert since the proposed computer-aided detection techniques are solely based on the medical images.